Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight.

ABSTRACT: Insect flight is a strongly nonlinear and actuated dynamical system. As such, strategies for understanding its control have typically relied on either model-based methods or linearizations thereof. Here we develop a framework that combines model predictive control on an established flight dynamics model and deep neural networks (DNN) to create an efficient method for solving the inverse problem of flight control. We turn to natural systems for inspiration since they inherently demonstrate network pruning with the consequence of yielding more efficient networks for a specific set of tasks. This bio-inspired approach allows us to leverage network pruning to optimally sparsify a DNN architecture in order to perform flight tasks with as few neural connections as possible, however, there are li

SUBMITTER: Zahn O 

PROVIDER: S-EPMC9543948 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature